During evolution proteins retain common three-dimentional structural features, even though the underlying sequence of amino acids can diverge dramatically. Such relationships can even be found internally within a given protein structure, arising from duplication and repetition of defined elements. Identifying relationships between two proteins, or two regions of the same protein, that are very distantly related, therefore, can be of extremely high value. Most often, identification of those relationships is needed on the level of primary amino acid codes, which is achieved by alignment of their sequences. However, when structures have been determined, such relationships can be detected by superposition of common regions, a technique known as structure alignment. Both procedures involve challenges, especially when the similarities between the two proteins are small. Consequently, there remains a need for methods that reliably and accurately compute sequence or structure alignments. In the past year, we have continued our efforts to address this need on two different fronts. First, we have made improvements to our benchmark set of homologous membrane protein structures, called HOMEP. The code used to compile the dataset has been rewritten to make it more streamlined and able to run in parallel, allowing for fast future updates as the database of available membrane protein structures continues its exponential growth. Although the updated version of HOMEP is not yet complete, we expect this process to finish shortly, at which time the dataset will be made available to the public through our website, www.forrestlab.org. These changes will facilitate retraining and therefore improvements of our sequence alignment software, AlignMe. Second, we have expanded an earlier (manual) analysis of symmetries within known structures of membrane proteins (Forrest, Annu Rev Biophys 2015), by initiating a systematic study to apply available symmetry analysis tools (SymD and CEsymm) to this new HOMEP dataset. This work is expected to identify patterns and relationships in symmetrical and asymmetrical membrane proteins that may speak to their functional mechanisms.